The Support Vector Machine (SVM) is an efficient tool in machine learning with high accuracy performance. However, in order to achieve the highest accuracy performance, n-fold cross validation is commonly used to identify the best hyperparameters for SVM. This becomes a weak point of SVM due to the extremely long training time for various hyperparameters of different kernel functions. In this p...

This paper presents a novel approach to classify agricultural crops using NDVI time series. The novelty lies in i) extracting a set of features from the each and every NDVI curve, and ii) using them to train a crop classification model using a Support Vector Machine (SVM). Specifically, we use the TIMESAT program package to: 1) smooth the time series, 2) decompose them into agricultural seasons...

The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implem...

Boost is a kind of method for improving the accuracy of a given learning algorithm by combining multiple weak learners to “boost” into a strong learner. The gist of AdaBoost is based on the assumption that even though a weak learner cannot do good for all classifications, each of them is good at some subsets of the given data with certain bias, so that by assembling many weak learner together, ...

With respect to the problem that it is difficult to pick out eggs with small cracks, a novel vibrating type of eggshell cracks detection system using a magnetostrictive transducer was developed firstly. Then the acoustic signals was recorded while the transducer vibrating with the swept frequency from 1 to 14000 Hz. Thirdly, the wavelet and Burg power spectrum were used for data pretreatment an...

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not conside...

Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker's emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional stat...

The ATP binding proteins exist as a hybrid of proteins with Walker A motif and universal stress proteins (USPs) having an alternative motif for binding ATP. There is an urgent need to find a reliable and comprehensive hybrid predictor for ATP binding proteins using whole sequence information. In this paper the open source LIBSVM toolbox was used to build a classifier at 10-fold cross-validation...

Functional annotation of a protein sequence in the absence of experimental data or clear similarity to a sequence of known function is difficult. In this study, a simple set of sequence attributes based on physicochemical and predicted structural characteristics were used as input to machine learning methods. In order to improve performance through increasing the data available for training, a ...

In prior works, stochastic dual coordinate ascent (SDCA) has been parallelized in a multi-core environment where the cores communicate through shared memory, or in a multi-processor distributed memory environment where the processors communicate through message passing. In this paper, we propose a hybrid SDCA framework for multi-core clusters, the most common high performance computing environm...